Minutes of the Meeting
Introductory Statistics for Economics Date: 27th October, 2022
Subject : B.A. (Hons) Economics – First Semester (2022) Course Code : ECON003
Course Abbreviation : STAT l
Credits : 4
Duration (per week) : 4 hours (3 Lectures+ 1 Tutorial) Date of Meeting : 27th October 2022
Venue : Delhi School of Economics
Chair : Prof. Pami Dua and Dr. Reetika Garg
Attended By:
Pawan Kumar Ramjas College
Yogita Yadav Sri Venkateswara College Anu Singh Deswal Jesus and Mary College Deepika Goel Aryabhatta college
Shweta Nanda ARSD
Renu Kumari Verma Motilal Nehru College Evening Ajay Kumar Kamala nehru college
Anuj Kumar Satyawati College(E)
Gurpinder Kaur Shaheed Bhagat Singh College Dr.D Appala Naidu ARSD
Sarweshwar Kumar Gautam Satyawati College Ankur Bhatnagar Satyawati College
Aishwarya Gargi College
Dr. Arun Kumar DCAC
Dr Pooja Sharma Daulat Ram College
Kanika Aggarwal Shri Ram college of commerce Yogita Yadav Sri Venkateswara College Rakesh Kumar Dyal Singh College
Shubhi Singh Lady Shri Ram College for Women Paramjeet Kaur Sri Guru Gobind Singh College of Commerce
Ramesh Kumar BNC
Course Objectives: The course familiarizes students with methods of summarizing and describing important features of data. The course teaches students the basics of probability theory and sets a necessary foundation for Inferential Statistical Theory and the Econometrics courses. The familiarity with probability theory will also be valuable for courses in economic theory.
Course Learning Outcomes: The student would understand the concept of probability, random variables and their distributions and become familiar with some commonly used discrete and continuous distributions of random variables so that they would be able to analyse various real-life data.
A meeting of teachers of this course was held to discuss the following:
The detailed reading list for the UGCF course to be implemented in the academic session 2022-23.
The pattern of the semester-end exam.
How to give a good intuition of the concepts to the students by using some practical concepts.
Since this paper is taught in its present form for the first time, under UGCF, a sub-committee was constituted to review the suggested readings. The committee consisted of the following teachers:
1 Deepika Goel, Aryabhatta College
2 Paramjeet Kaur, Sri Guru Gobind Singh College of Commerce 3 Priyanka Bhatia, SRCC College
4 Poonam Kalra, St. Stephen’s College 5 Pooja Sharma, Daulat Ram College The issues discussed in the meeting are as follows:
1. Teachers suggested that from the suggested readings in the syllabus, J Devore and Hogg, Tanis and Zimmerman (HT&Z) could be used as core textbooks. John Rice could be a suggested reading for the teachers
2. Chapter-wise exclusions are reported in the table given below for detailed readings.
3. Moment Generating Function (MGF)treatment of distributions is excluded.
4. Students should be aware of the following topics; however, no questions should be asked in the examinations:
a) Stem and Leaf Display
b) Only analytical questions on pictorial representation should be asked in the exams c) Double integration for Joint continuous random variables in Ch 5 should be kept simple.
5. In order to achieve uniformity in evaluation of final answer scripts, it was decided to include the following note in final question paper:
a) All questions within each section are to be answered in a contiguous manner on the answer sheet. Start each question on a new page, and all sub-parts of a question should follow one after the other.
b) All intermediate calculations should be rounded off to 3 decimal places. The values provided in statistical tables should not be rounded off. All final calculations should be rounded off to two decimal places.
6. It was agreed that the question paper will include internal choice in each section with limited number of sub-parts.
7. The following distribution of topics, indicative weightage, and the amount of choice within each section, was agreed upon:
a. Section 1: Unit 1 and Unit 2: (weightage 20 marks), Two questions of 10 marks each with one question from Unit 1 and the other from Unit 2. Internal choice in these units should be given as 2 out of 3 questions
b. Section 2: Unit 3: (weightage 20 marks), Two questions out of Three for 10 marks each.
c. Section 3: Unit 4: weightage 20 marks), Two questions out of Three for 10 marks each.
d. Section 4: Unit 5: (weightage 15 marks), Three questions out of Four for 5 marks each.
8. There would be no compulsory question in any of the sections.
9. The internal assessment would comprise of 10 marks Class test, 10 marks Class test/assignment. Attendance will carry 05 marks.
The details of the Course Content, Topic-wise Reading list, recommended textbooks are given below:
Content (Unit-wise):
Unit 1: Introduction and overview
The distinction between populations and samples and, between population parameters and sample statistics; Pictorial Methods in Descriptive Statistics; Measures of
Location and Variability.
Unit 2: Elementary probability theory
Sample spaces and events; probability axioms and properties; counting techniques;
conditional probability and Bayes' rule; independence.
Unit 3: Random variables and probability distributions
Defining random variables; discrete and continuous random variables, probability distributions; expected values and functions of random variables.
Unit 4: Special Probability Distributions
Properties of commonly used discrete and continuous distributions (uniform, binomial, exponential, Poisson, hypergeometric and Normal random variables).
Unit 5: Random sampling and jointly distributed random variables
Density and distribution functions for jointly distributed random variables; computing expected values of jointly distributed random variables; conditional distributions and expectations, covariance and correlation.
Essential Readings:
1. Devore, J. (2012). Probability and Statistics for Engineers, 8th ed. Cengage Learn-ing.
2. Hogg, R., Tanis, E., Zimmerman, D. (2021) Probability and Statistical inference, 10th Edition, Pearson.
3. Miller, I., Miller, M. (2017). J. Freund's Mathematical Statistics with Applications, 8th ed. Pearson.
Recommended Readings for Teachers:
Unit
No. TOPIC READINGS FROM CORE TEXTS
1. Introduction and
Overview Devore: Ch 1
2.
Elementary Probability
Theory Devore: Ch 2
Hogg, Tanis and Zimmerman: Ch 1
3.
Random Variables and Probability Distributions
Devore: Ch 3 (3.1-3.3), Ch 4 (4.1-4.2)
Hogg, Tanis and Zimmerman: Ch 2 (2.1-2.2), Ch 3.1
4. Special Probability Distributions
Devore: Ch 3 (3.4-3.6) except negative binomial distribution, Ch 4 (4.3-4.4) except gamma
distribution
Hogg, Tanis and Zimmerman: Ch 2 (2.4, 2.5 and 2.7), Ch 3 (3.2-3.3) except gamma and chi-square distributions (MGF treatment of distributions is not
included)
5.
Random Sampling and Jointly Distributed Random Variables
Devore: Ch 5.1-5.3 (except pgs 200-202), 5.4, 5.5
Hogg, Tanis and Zimmerman: Ch 4 (4.1-4.4) [Double integration can be kept simple]
1. John A. Rice (2007). Mathematical Statistics and Data Analysis, 3rd ed. Thomson Brooks/Cole.
2. Gelman, A., & Nolan, D. (2017). Teaching statistics: A bag of tricks. Oxford University Press.
